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Remote Sensing
Volume 12
Issue 10
10.3390/rs12101642
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Article

Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method

by
Pasquale Cascarano
1,‡,
Francesco Corsini
2,‡,
Stefano Gandolfi
2,‡,
Elena Loli Piccolomini
3,‡,
Emanuele Mandanici
2,*,†,‡,
Luca Tavasci
2,‡ and
Fabiana Zama
1
1
Department of Mathematics, University of Bologna, 40127 Bologna, Italy
2
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
3
Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy
*
Author to whom correspondence should be addressed.
†
Current address: DICAM, viale del Risorgimento 2, 40136 Bologna, Italy.
‡
These authors contributed equally to this work.
Remote Sens. 2020, 12(10), 1642; https://doi.org/10.3390/rs12101642
Received: 23 April 2020 / Revised: 15 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
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Abstract

The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a single image or multiple images of the target surface are available. In this perspective, we propose a new super-resolution algorithm, which can handle either single or multiple images. It is based on a total variation regularization approach and implements a fully automated choice of all the parameters, without any training dataset nor a priori information. Through simulations, the accuracy of the generated super-resolution images was assessed, in terms of both global statistical indicators and analysis of temperature errors at hot and cold spots. The algorithm was tested and applied to aerial and terrestrial thermal images. Results and comparisons with state-of-the-art methods confirmed an excellent compromise between the quality of the high-resolution images obtained and the required computational time. View Full-Text
Keywords: super-resolution; thermal images; regularized reconstruction; total variation regularization; automatic regularization super-resolution; thermal images; regularized reconstruction; total variation regularization; automatic regularization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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MDPI and ACS Style

Cascarano, P.; Corsini, F.; Gandolfi, S.; Piccolomini, E.L.; Mandanici, E.; Tavasci, L.; Zama, F. Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method. Remote Sens. 2020, 12, 1642. https://doi.org/10.3390/rs12101642

AMA Style

Cascarano P, Corsini F, Gandolfi S, Piccolomini EL, Mandanici E, Tavasci L, Zama F. Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method. Remote Sensing. 2020; 12(10):1642. https://doi.org/10.3390/rs12101642

Chicago/Turabian Style

Cascarano, Pasquale, Francesco Corsini, Stefano Gandolfi, Elena Loli Piccolomini, Emanuele Mandanici, Luca Tavasci, and Fabiana Zama. 2020. "Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method" Remote Sensing 12, no. 10: 1642. https://doi.org/10.3390/rs12101642

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